Mechanical Behavior of Ultrahigh-Performance Concrete Tunnel Lining Segments
Bibliographic record
Abstract
Ultrahigh-performance concrete (UHPC) is a novel material demonstrating superior mechanical, durability and sustainability performance. However, its implementation in massive structures is hampered by its high initial cost and the lack of stakeholders' confidence, especially in developing countries. Therefore, the present study explores, for the first time, a novel application of UHPC, incorporating hybrid steel fibers in precast tunnel lining segments. Reduced scale curved tunnel lining segments were cast using UHPC incorporating hybrid 8 mm and 16 mm steel fibers at dosages of 1%, 2% and 3% by mixture volume. Flexural and thrust load tests were conducted to investigate the mechanical behavior of UHPC tunnel lining segments thus produced. It was observed that the flow of UHPC mixtures decreased due to steel fibers addition, yet steel fibers increased the mechanical and durability properties. Flexural tests on lining segments showed that both the strain hardening (multiple cracking) and strain softening (post-peak behavior) phases were enhanced due to hybrid addition of steel fibers in comparison with the control segments without fibers. Specimens incorporating 3% of hybrid steel fibers achieved 57% increase in ultimate load carrying capacity and exhibited multiple cracking patterns compared to that of identical UHPC segments with 1% fibers. Moreover, segments without fibers incurred excessive cracking and spalling of concrete at the base under the thrust load test. However, more stable behavior was observed for segments incorporating steel fibers under the thrust load, indicating its capability to resist typical thrust loads during tunnel lining field installation. This study highlights the potential use of UHPC with hybrid steel fibers for improved structural behavior. Moreover, the use of UHPC allows producing structural members with reduced cross-sectional dimensions, leading to reduced overall structural weight and increased clear space.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".